Perception is an important component of Automatic Driving (AD) as well as for many real life applications and devices which need real time information about the occupation of a given space. Through perception, devices gain crucial information about empty spaces, occupied spaces and information about changes of the environment around the them. However, since the environment around devices may be very dynamic, it is essential for the perception process to be as fast and accurate as possible.
Occupancy Grids are a perception technology that proved to be effective at combining sensors information to identify static and dynamic obstacles as well as free spaces in a given area simultaneously. They are based on so-called particles that identify the likelihood that a given area in the occupancy grid is occupied. In general, an increase of the number of particles in occupancy grids results in an increase of the perception precision; on the other hand, an increase of particles also results in an increase in computation requirements and ultimately in a reduction of perception speed.